System and method for learning driver preference and adapting lane centering controls to driver behavior

ABSTRACT

A vehicle and a system and method of operating a vehicle. The system includes a processor. The processor learns a driver&#39;s behavior of a driver of the vehicle as the driver navigates a road segment, creates a behavior policy based on the driver&#39;s behavior and a threshold associated with the road segment, and controls the vehicle to navigate the road segment using the behavior policy.

INTRODUCTION

The subject disclosure relates to vehicle navigation and, in particular,to a system and method for adapting a behavior of an autonomous vehicleto a behavior of a driver of the vehicle.

An autonomous vehicle or semi-autonomous vehicle is able to navigateroads and highways according to a pre-determined behavior. The exactbehavior of the autonomous vehicle will most likely differ from thebehavior of the vehicle when driven by a person behind the wheel. Forexample, the autonomous vehicle will often navigate a curve bymaintaining itself midway between an outer edge and an inner edge of thecurve, while a driver may hug either the outer edge or inner edge. It isdesirable however that the driver be comfortable with the way theautonomous vehicle behaves. Accordingly, it is desirable to train anautonomous vehicle to adapt its behavior to imitate that of the driver.

SUMMARY

In one exemplary embodiment, a method of operating a vehicle isdisclosed. A driver's behavior of a driver of the vehicle is learned ata processor as the driver navigates a road segment. A behavior policy iscreated at the processor based on the driver's behavior and a thresholdassociated with the road segment. The processor controls the vehicle tonavigate the road segment using the behavior policy.

In addition to one or more of the features described herein, a knowledgematrix is constructed by learning the driver's behavior for a pluralityof navigations of the road segment, selecting an action from theknowledge matrix based on an environment state and creating the behaviorpolicy based on the selected action. The knowledge matrix is based on atleast one of the environmental state, a vehicle state, and a driverstate. Learning the driver's behavior further includes measuring atleast one of a vehicle speed selected by the driver, a lateral controlof the vehicle selected by the driver, and an acceleration ordeceleration of the vehicle selected by driver when the driver navigatesthe vehicle over the road segment. In an embodiment, the thresholdassociated with the road segment includes a safety limit of the roadsegment, and creating the behavior policy includes modifying the learneddriver's behavior based on the safety limit of the road segment. Thedriver's behavior includes at least one of the driver's behavior withina lane of the road segment, and the driver's behavior for changing lanesin the road segment. The method further includes learning the driver'sbehavior in one of an offline learning mode in which the vehicle isdriven by the driver and an online learning mode in which the vehicle iscontrolled by the processor as the driver operates a control of thevehicle.

In another exemplary embodiment, a system for operating a vehicle isdisclosed. The system includes a processor configured to learn adriver's behavior of a driver of the vehicle as the driver navigates aroad segment, create a behavior policy based on the driver's behaviorand a threshold associated with the road segment, and control thevehicle to navigate the road segment using the behavior policy.

In addition to one or more of the features described herein, theprocessor is further configured to construct a knowledge matrix bylearning the driver's behavior for a plurality of navigations of theroad segment, select an action from the knowledge matrix based on anenvironment state and create the behavior policy based on the selectedaction. The knowledge matrix is based on at least one of theenvironmental state, a vehicle state, and a driver state. The processoris further configured to learn the driver's behavior by measuring atleast one of a vehicle speed selected by the driver, a lateral controlof the vehicle selected by the driver, and an acceleration ordeceleration of the vehicle selected by the driver when the drivernavigates the vehicle over the road segment. The threshold associatedwith the road segment includes a safety limit of the road segment, andthe processor is configured to create the behavior policy by modifyingthe learned driver's behavior based on the safety limit of the roadsegment. In an embodiment, the system further includes an electronicspackage transferable to and from the vehicle. The processor is furtherconfigured to learn the driver's behavior in one of an offline learningmode in which the vehicle is driven by the driver and an online learningmode in which the vehicle is controlled by the processor as the driveroperates a control of the vehicle.

In yet another exemplary embodiment, a vehicle is disclosed. The vehicleincludes a processor. The processor is configured to learn a driver'sbehavior of a driver of the vehicle as the driver navigates a roadsegment, create a behavior policy based on the driver's behavior and athreshold associated with the road segment, and control the vehicle tonavigate the road segment using the behavior policy.

In addition to one or more of the features described herein, theprocessor is further configured to construct a knowledge matrix bylearning the driver's behavior for a plurality of navigations of theroad segment, select an action from the knowledge matrix based on anenvironment state and create the behavior policy based on the selectedaction. The vehicle further includes an additional processor that istransferable to and from the vehicle, the additional processorconfigured to modify a path planning command based on the learneddriver's behavior and/or to adapt a lane centering control command tothe learned-driver-behavior. The processor is further configured tolearn the driver's behavior by measuring at least one of a vehicle speedselected by the driver, a lateral control of the vehicle selected by thedriver, and an acceleration or deceleration of the vehicle selected bythe driver when the driver navigates the vehicle over the road segment.The threshold associated with the road segment includes a safety limitof the road segment, and the processor is configured to create thebehavior policy by modifying the learned driver's behavior based on thesafety limit of the road segment. The processor is further configured tolearn the driver's behavior in one of an offline learning mode in whichthe vehicle is driven by the driver and an online learning mode in whichthe vehicle is controlled by the processor as the driver operates acontrol of the vehicle.

The above features and advantages, and other features and advantages ofthe disclosure are readily apparent from the following detaileddescription when taken in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, advantages and details appear, by way of example only,in the following detailed description, the detailed descriptionreferring to the drawings in which:

FIG. 1 shows a vehicle in accordance with an exemplary embodiment;

FIG. 2 shows a flowchart illustrating a method for training a vehicle tonavigate a road segment with a behavior that imitates the behavior of aselected driver;

FIG. 3 shows a top view of a road segment including a curved portion;

FIG. 4 shows a top view of the road segment of FIG. 3;

FIGS. 5A-5E illustrate various driving behaviors that can be exhibitedby a driver;

FIG. 6 shows a schematic diagram of a behavior learning and modificationsystem suitable for learning a driver's behavior and operating theautonomous vehicle so as to imitate the driver's behavior;

FIG. 7 shows a flowchart illustrating a method by which the systemlearns the behavior of a driver and subsequently navigates the vehiclebased on the learned behavior;

FIG. 8 shows a top view of a roadway illustrating a learning operationof the autonomous vehicle;

FIG. 9 shows a classification of the road and environmental factor forthe roadway of FIG. 8; and

FIG. 10 shows a reward profile that can be quantified over the roadwayof FIG. 8.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, its application or uses. Itshould be understood that throughout the drawings, correspondingreference numerals indicate like or corresponding parts and features. Asused herein, the term module refers to processing circuitry that mayinclude an application specific integrated circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and memory thatexecutes one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

In accordance with an exemplary embodiment, FIG. 1 shows a vehicle 10.In an exemplary embodiment, the vehicle 10 is a semi-autonomous orautonomous vehicle. In various embodiments, the vehicle 10 includes atleast one driver assistance system for both steering andacceleration/deceleration using information about the drivingenvironment, such as cruise control and lane-centering. While the drivercan be disengaged from physically operating the vehicle 10 by having hisor her hands off the steering wheel and foot off the pedal at the sametime, the driver must be ready to take control of the vehicle.

In general, a trajectory planning system 100 determines a trajectoryplan for automated driving of the vehicle 10. The vehicle 10 generallyincludes a chassis 12, a body 14, front wheels 16, and rear wheels 18.The body 14 is arranged on the chassis 12 and substantially enclosescomponents of the vehicle 10. The body 14 and the chassis 12 may jointlyform a frame. The wheels 16 and 18 are each rotationally coupled to thechassis 12 near respective corners of the body 14.

As shown, the vehicle 10 generally includes a propulsion system 20, atransmission system 22, a steering system 24, a brake system 26, asensor system 28, an actuator system 30, at least one data storagedevice 32, at least one controller 34, and a communication system 36.The propulsion system 20 may, in various embodiments, include aninternal combustion engine, an electric machine such as a tractionmotor, and/or a fuel cell propulsion system. The transmission system 22is configured to transmit power from the propulsion system 20 to thevehicle wheels 16 and 18 according to selectable speed ratios. Accordingto various embodiments, the transmission system 22 may include astep-ratio automatic transmission, a continuously-variable transmission,or other appropriate transmission. The brake system 26 is configured toprovide braking torque to the vehicle wheels 16 and 18. The brake system26 may, in various embodiments, include friction brakes, brake by wire,a regenerative braking system such as an electric machine, and/or otherappropriate braking systems. The steering system 24 influences aposition of the vehicle wheels 16 and 18. While depicted as including asteering wheel for illustrative purposes, in some embodimentscontemplated within the scope of the present disclosure, the steeringsystem 24 may not include a steering wheel.

The sensor system 28 includes one or more sensing devices 40 a-40 n thatsense observable conditions of the exterior environment and/or theinterior environment of the vehicle 10. The sensing devices 40 a-40 ncan include, but are not limited to, radars, lidars, global positioningsystems, optical cameras, thermal cameras, ultrasonic sensors, and/orother sensors for observing and measuring parameters of the exteriorenvironment. The sensing devices 40 a-40 n may further include brakesensors, steering angle sensors, wheel speed sensors, etc. for observingand measuring in-vehicle parameters of the vehicle. The cameras caninclude two or more digital cameras spaced at a selected distance fromeach other, in which the two or more digital cameras are used to obtainstereoscopic images of the surrounding environment in order to obtain athree-dimensional image. The actuator system 30 includes one or moreactuator devices 42 a-42 n that control one or more vehicle featuressuch as, but not limited to, the propulsion system 20, the transmissionsystem 22, the steering system 24, and the brake system 26. In variousembodiments, the vehicle features can further include interior and/orexterior vehicle features such as, but not limited to, doors, a trunk,and cabin features such as air, music, lighting, etc. (not numbered).

The at least one controller 34 includes at least one processor 44 and acomputer readable storage device or media 46. The at least one processor44 can be any custom made or commercially available processor, a centralprocessing unit (CPU), a graphics processing unit (GPU), an auxiliaryprocessor among several processors associated with the at least onecontroller 34, a semiconductor-based microprocessor (in the form of amicrochip or chip set), a macroprocessor, any combination thereof, orgenerally any device for executing instructions. The computer readablestorage device or media 46 may include volatile and nonvolatile storagein read-only memory (ROM), random-access memory (RAM), and keep-alivememory (KAM), for example. KAM is a persistent or non-volatile memorythat may be used to store various operating variables while the at leastone processor 44 is powered down. The computer-readable storage deviceor media 46 may be implemented using any of a number of known memorydevices such as PROMs (programmable read-only memory), EPROMs(electrically PROM), EEPROMs (electrically erasable PROM), flash memory,or any other electric, magnetic, optical, or combination memory devicescapable of storing data, some of which represent executableinstructions, used by the at least one controller 34 in controlling thevehicle 10.

The instructions may include one or more separate programs, each ofwhich comprises an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theat least one processor 44, receive and process signals from the sensorsystem 28, perform logic, calculations, methods and/or algorithms forautomatically controlling the components of the vehicle 10, and generatecontrol signals to the actuator system 30 to automatically control thecomponents of the vehicle 10 based on the logic, calculations, methods,and/or algorithms. Although only one controller is shown in FIG. 1,embodiments of the vehicle 10 can include any number of controllers thatcommunicate over any suitable communication medium or a combination ofcommunication mediums and that cooperate to process the sensor signals,perform logic, calculations, methods, and/or algorithms, and generatecontrol signals to automatically control features of the vehicle 10.

The method disclosed herein operates a vehicle autonomously according toa learned behavior or behavior policy that is based on observations of adriver's behavior. In one embodiment, the method disclosed herein can beperformed on the processor 44. In an alternate embodiment, a separatedriving behavior system 50 can be affixed to the vehicle andcommunicatively coupled with vehicle electronics such as processor 44.The driver behavior system 50 performs the methods for autonomousdriving of the vehicle by observing the driver's behavior and basing itsbehavior policy for autonomous driving on the driver's behavior. Invarious embodiments, the driver behavior system 50 modifies a pathplanning command based on the learned driver's behavior and/or adapts alane centering control command to the learned-driver's behavior. Thedriver behavior system 50 can be an electronics package, or processorthat can be added to or removed from the vehicle 10 as desired and istransferrable to and from the vehicle.

The communication system 36 is configured to wirelessly communicateinformation to and from other entities 48, such as but not limited to,other vehicles (“V2V” communication) infrastructure (“V2I”communication), remote systems, and/or personal devices. In an exemplaryembodiment, the communication system 36 is a wireless communicationsystem configured to communicate via a wireless local area network(WLAN) using IEEE 802.11 standards or by using cellular datacommunication. However, additional or alternate communication methods,such as a dedicated short-range communications (DSRC) channel, are alsoconsidered within the scope of the present disclosure. DSRC channelsrefer to one-way or two-way short-range to medium-range wirelesscommunication channels specifically designed for automotive use and acorresponding set of protocols and standards.

FIG. 2 shows a flowchart 200 illustrating a method for training avehicle to navigate a road segment with a behavior that imitates thebehavior of a selected driver. A driver's behavior is characterized byhow the driver navigates a particular road segment or type of roadsegment. The driver's behavior over a road segment can be quantified byvarious parameters, such as a speed or average speed of the vehiclebeing driven, the relative location of the vehicle within the lane(i.e., centered, to the left, to the right), etc. For a vehicle changinglanes, the driver's behavior can be quantified by how abruptly orsmoothly the driver changes lanes, varies speed, etc. The driver'sbehavior can be determined by recording the driver's speed, lateralcontrol or steering commands, etc. at the various sensing devices 40a-40 n of the vehicle.

In box 202, an environmental state of the vehicle is determined. Theenvironmental state of the vehicle can be based on a geometry of theroad or road segment being traversed or navigated by the vehicle, apresence of obstacles as well as their relative locations and speeds,etc. In box 204, the vehicle learns a driver's behavior for theenvironmental state as the driver navigates the vehicle over the roadsegment. The driver's behavior includes a vehicle speed and lateralcontrol, etc. In box 206, the learned behavior is used to build up orconstruct a knowledge base for the driver. In box 208, the autonomousvehicle subsequently drives over the road segment using a behaviorpolicy based on the knowledge matrix.

The driver's behavior can be learned offline or online. In an offlinelearning mode, the processor 44 records the driver's behavior while thedriver is in complete control of the vehicle, (i.e., no autonomousdriving modes are activated). In an online learning mode, the processor44 operates the vehicle according to its pre-determined drivingbehavior, while the driver is simultaneously operating the steeringwheel. The processor 44 records any differences between the driver'sbehavior and the behavior of the autonomous vehicle and adapts thebehavior of the vehicle (“vehicle behavior”) to that of the driver,within safe and stable driving behavior limits.

While described herein as learning a driving behavior of a singledriver, the processor 44 can also identify a driver using data from asuitable identification sensor associated with the vehicle. Theprocessor 44 can thereby learn the driving behavior of multiple driversand can change its behavior to accommodate the driving behavior of thedriver currently behind the wheel.

FIG. 3 shows a top view 300 of a road segment 310 including a curvedportion. The top view 300 shows a vehicle 10 and a lane-centeringcontrol trajectory 302 that is selected by the autonomous vehicle 10 tonavigate the road segment 310 by maintaining the vehicle 10 in thecenter of the lane, particularly over the curved portion. Also shown isa driver's desired trajectory 304 that is taken by the driver over theroad segment 310. The driver's desired trajectory 304 hugs the inneredges of the curve over the curved portion of the road segment. Thedifference between the lane-centering control trajectory 302 and thedriver's desired trajectory 304 is recorded in order to learn thedriver's behavior.

FIG. 4 shows a top view 400 of the road segment 310 of FIG. 3. The topview 400 shows an updated lane centering control trajectory 402 that isbased on a learning algorithm applied to the driver's desired trajectory304 of FIG. 3. The updated lane centering control trajectory 402deviates from the lane-centering control trajectory 302 of FIG. 3 inorder to be more aligned with the driver's desired trajectory 304. Theupdated lane centering control trajectory 402 lies within safety limits404 for the road segment set by the processor, which define a width ofthe road segment 310 that is safely away from edges of the road segment.The updated lane centering control trajectory 402 is based on modifyingthe driver's desired trajectory to lie within the safety limits 404 ofthe road segment. The safety limits can define a road section boundary,a maximum vehicle speed, a maximum acceleration or deceleration withinthe road segment, etc. In an embodiment in which an updated lanecentering control trajectory 402 is limited by the safety limits 404,the updated lane centering control trajectory 402 does not completelyimitate the driver's desired trajectory 304.

FIGS. 5A-5E illustrate various driving behaviors that can be exhibitedby a driver. FIG. 5A illustrates a driver in a lane adjacent to a laneof oncoming traffic. The driver veers temporarily to one side (i.e., theright side) of the vehicle lane as a remote vehicle is approaching inthe oncoming lane (as indicated by arrow 502) in order to place distancebetween the vehicle and the remote vehicle. FIG. 5B shows a vehiclealong a rural road, with the driver tending to drive offset to one sideof the lane (i.e., the outside of the lane, as indicated by arrow 504).FIG. 5C shows a multi-lane road with a vehicle in a far-left lane of themulti-lane road hugging the side barrier of the lane (as indicated byarrow 506). FIG. 5D show a lane with cones in an adjacent lane and avehicle maneuvering to a side of the lane to get away from the cones (asindicated by arrow 508). FIG. 5E illustrates a vehicle along a curvedsection of a roadway. Some drivers may tend to hug an inside of thecurve while other drivers may tend to hug an outside of the curve (asindicated by arrow 510).

FIG. 6 shows a schematic diagram of a behavior learning and modificationsystem 600 suitable for learning a driver's behavior and operating theautonomous vehicle so as to imitate the driver's behavior. The system600 includes a sensor module 602, vehicle and environment module 604 anda path planning module 606. The sensor module 602 includes variouscameras, Lidar, radar or other sensors for determining the state of thesurroundings of the vehicle with respect to the road, as well as thelocation of remote vehicles, pedestrians, obstacles, etc. The vehicleand environment module 604 provides data from the environment such as aroad geometry, a location, speed and orientation of vehicles and otherobstacles in the environment etc. The data from the sensor module 602and the vehicle and environment module 604 is provided to the pathplanning module 606 which plans a selected path or trajectory for theautonomous vehicle.

The system 600 further includes a learning module 608 and an adaptivecontrol module 610 for learning the behavior of the driver andimplementing the learned behavior at the vehicle. The learning module608 receives driver steering signal from a steering or control sensor612, a steering signal taken by the vehicle from the adaptive controlmodule 610 as well as state data S_(i) from the vehicle and environmentmodule 604. The state data S_(i) for an i^(th) road segment can be asshown in Eq. (2):

S _(i)=[S _(p,i) S _(ρ,i) S _({dot over (ρ)},ι)]  Eq. (1)

where S_(p,i) is a position state, S_(ρ,i) is a curvature of the roadstate and S_({dot over (p)},i) is a change rate of the curvature of theroad state.

By comparing the driver input to the current trajectory for the currentstate data S_(i), the learning module 608 determines a reward P(i,j) forthe autonomous vehicle. The reward P(i,j) quantifies an agreement orlack of agreement between vehicle trajectory and driver trajectory. Thelearning module 608 updates a knowledge matrix Q(i,j) based on thedetermined reward as given by the following equation:

Q(i,j)=αP(i,j)+(1−α)Q(i,j)  Eq. (2)

\where α=a user-selected learning rate for updating the knowledgematrix.

The adaptive control module 610 receives a planned trajectory from thepath planning module 606, state data S_(i) from the vehicle andenvironment module 604, and a best action policy A_(j) from the learningmodule 608. The best action policy A_(j) is derived from the knowledgematrix Q(i,j). The knowledge matrix is built upon monitoring variousactions A_(j) for a plurality of states S_(i):

$\begin{matrix}{\mspace{121mu}{{S_{1}\mspace{34mu}\ldots\mspace{31mu} S_{n}}{Q = {\begin{matrix}A_{1} \\\vdots \\A_{m}\end{matrix}\begin{bmatrix}Q_{11} & \ldots & Q_{n\; 1} \\\vdots & \ddots & \vdots \\Q_{1m} & \ldots & Q_{nm}\end{bmatrix}}}}} & {{Eq}.\mspace{14mu}(3)}\end{matrix}$

The adaptive control module 610 calculates a behavior policy thatincludes steering signal δ_(u) based on the input data. The steeringsignal δ_(u) can be indicated by the following equation:

δ_(u) =K(δ_(q))·e  Eq. (4)

where K is a matrix of entries that are functions of δ_(q). In analternate embodiment, the behavior policy and steering signal δ_(u) aregiven by Eq. (5):

δ_(u) =K·e+δ _(q)  Eq. (5)

where K is a design parameter for lane following control, and e is apredictive error based on various parameter, such as the vehicle'slateral position and heading, road curvature, a control input, thedriver's input, the learned control input, a desired road wheel angle, acontroller torque and a driver torque. The driver input torque δ_(q) isa function of the selected action:

δ_(q) =f(A _(j))  Eq. (6)

The adaptive control module 610 provides the behavior policy andsteering signal δ_(u) to the steering module 614 as well as to thelearning module 608. The steering module 614 steers the vehicle usingthe behavior policy and steering signal.

FIG. 7 shows a flowchart 700 illustrating a method by which the system600 learns the behavior of a driver and subsequently navigates thevehicle based on the learned behavior.

In box 701, a driver's action or input is received. In box 702, thedriver's behavior is quantified based on the driver's action or input,as signified by vector q. In box 704, the system 600 evaluates whetherthe driver's behavior is to be used for learning purposes. When q isgreater than or equal to a selected threshold value, the method proceedsto box 705 where the driver's behavior is not used for learning and isused only for driving the vehicle. However, when q<threshold, the system600 learns from the driver's behavior.

In box 706, the environment state and a corresponding action to be takenby the vehicle is estimated. In box 708, a reward function P(i,j) iscalculated based on the estimated state and action. At box 708 thedriver's input and a road awareness is received from box 712 and theestimated action of the vehicle is received from box 706. The rewardfunction P(i,j) is calculated based on this input. In box 710 aknowledge matrix Q(i,j) is updated based on the calculated reward andthe desired learning rate.

In box 714, a policy A_(j) is selected form the knowledge matrixdetermined in box 710 and an environment state from box 712. The policyand environment state are used to calculate an action for navigating thevehicle.

In box 716 a stability check for the selected action is performed inorder to ensure that the action lies within safe driving requirements.If the action performed by the selected policy is greater than a safetythreshold, then the method returns to box 714 in order to obtain anupdated action. If the action performed by the selected policy is withinthe safety threshold, then the method proceeds to box 720. At box 720,the control action (and hence the behavior policy) is applied to thevehicle.

FIG. 8 shows a top view of a roadway 800 illustrating a learningoperation of the autonomous vehicle. The roadway 800 includes a leftlane marker 802 and a right lane marker 804. A first segment (segment A)of the roadway 800 extends in a straight line for about 25 meters. Atabout x=25 meters, the roadway 800 turns left and continues straightalong a second segment (segment B). At about x=32.5 meters, the roadway800 turns right and continues straight along a third segment (segmentC). At about x=40 meters, the roadway 800 turns left again and continuesstraight along a fourth segment (segment D). Segment D travels in thesame direction as Segment A.

A first vehicle trajectory 806 selected by the autonomous vehicle isshown to maintain an equal distance between the left lane marker 802 andthe right lane marker 804 over all segments of the roadway 800. A secondvehicle trajectory 808 selected by a driver of the vehicle is shown tostay in the center of the lane for Segment A. However, in Segment B, thesecond vehicle trajectory strays from the center toward the right lanemarker 804. In Segment C, the second vehicle trajectory strays towardsthe left lane marker 802. In Segment D, the second vehicle trajectory isback in the center of the lane.

FIG. 9 shows a classification of the road and environmental factor forthe roadway 800 of FIG. 8. A position error state 902 is recordedbetween the path of the autonomous vehicle and the path of the driver. Acurvature state 904 of the roadway 800 is also recorded as well as acurvature rate of change state 906 for the roadway 800. These positionerror state 902, curvature state 904, and curvature rate of change state906 are respective components of the state variable of Eq. (1).

FIG. 10 shows a reward profile that can be quantified over the roadway800 of FIG. 8. The reward profile shows a maximum reward over segmentsin which the driver's trajectory and the vehicle selected trajectory arethe same (i.e., straight segment A and segment D). The reward profiledecreases over the roadway segments where the driver's selectedtrajectory differs from the vehicle's selected trajectory (i.e., segmentB and segment C)

While the above disclosure has been described with reference toexemplary embodiments, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substituted forelements thereof without departing from its scope. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the disclosure without departing from the essentialscope thereof. Therefore, it is intended that the present disclosure notbe limited to the particular embodiments disclosed, but will include allembodiments falling within the scope thereof

What is claimed is:
 1. A method of operating a vehicle, comprising:learning, at a processor, a driver's behavior of a driver of the vehicleas the driver navigates a road segment; creating, at the processor, abehavior policy based on the driver's behavior and a thresholdassociated with the road segment; and controlling, via a processor, thevehicle to navigate the road segment using the behavior policy.
 2. Themethod of claim 1, further comprising constructing a knowledge matrix bylearning the driver's behavior for a plurality of navigations of theroad segment, selecting an action from the knowledge matrix based on anenvironment state and creating the behavior policy based on the selectedaction.
 3. The method of claim 2, wherein the knowledge matrix is basedon at least one of the environmental state, a vehicle state, and adriver state.
 4. The method of claim 1, wherein learning the driver'sbehavior further comprises measuring at least one of a vehicle speedselected by the driver, a lateral control of the vehicle selected by thedriver, and an acceleration or deceleration of the vehicle selected bydriver when the driver navigates the vehicle over the road segment. 5.The method of claim 1, wherein the threshold associated with the roadsegment includes a safety limit of the road segment, and creating thebehavior policy includes modifying the learned driver's behavior basedon the safety limit of the road segment.
 6. The method of claim 1,wherein the driver's behavior further comprises at least one of: thedriver's behavior within a lane of the road segment; and the driver'sbehavior for changing lanes in the road segment.
 7. The method of claim1, further comprising learning the driver's behavior in one of anoffline learning mode in which the vehicle is driven by the driver andan online learning mode in which the vehicle is controlled by theprocessor as the driver operates a control of the vehicle.
 8. A systemfor operating a vehicle, comprising: a processor configured to: learn adriver's behavior of a driver of the vehicle as the driver navigates aroad segment; create a behavior policy based on the driver's behaviorand a threshold associated with the road segment; and control thevehicle to navigate the road segment using the behavior policy.
 9. Thesystem of claim 8, wherein the processor is further configured toconstruct a knowledge matrix by learning the driver's behavior for aplurality of navigations of the road segment, select an action from theknowledge matrix based on an environment state and create the behaviorpolicy based on the selected action.
 10. The system of claim 9, whereinthe knowledge matrix is based on at least one of the environmentalstate, a vehicle state, and a driver state.
 11. The system of claim 8,wherein the processor is further configured to learn the driver'sbehavior by measuring at least one of a vehicle speed selected by thedriver, a lateral control of the vehicle selected by the driver, and anacceleration or deceleration of the vehicle selected by the driver whenthe driver navigates the vehicle over the road segment.
 12. The systemof claim 8, wherein the threshold associated with the road segmentincludes a safety limit of the road segment, and the processor isconfigured to create the behavior policy by modifying the learneddriver's behavior based on the safety limit of the road segment.
 13. Thesystem of claim 8, further comprising an electronics packagetransferable to and from the vehicle.
 14. The system of claim 8, whereinthe processor is further configured to learn the driver's behavior inone of an offline learning mode in which the vehicle is driven by thedriver and an online learning mode in which the vehicle is controlled bythe processor as the driver operates a control of the vehicle.
 15. Avehicle, comprising: a processor configured to: learn a driver'sbehavior of a driver of the vehicle as the driver navigates a roadsegment; create a behavior policy based on the driver's behavior and athreshold associated with the road segment; and control the vehicle tonavigate the road segment using the behavior policy.
 16. The vehicle ofclaim 15, wherein the processor is further configured to construct aknowledge matrix by learning the driver's behavior for a plurality ofnavigations of the road segment, select an action from the knowledgematrix based on an environment state and create the behavior policybased on the selected action.
 17. The vehicle of claim 15, furthercomprising an additional processor that is transferable to and from thevehicle, the additional processor configured to perform at least one of:(i) modifying the path planning commands based on the learned driver'sbehavior; and (ii) adapting a lane centering control command to thelearned-driver-behavior.
 18. The vehicle of claim 15, wherein theprocessor is further configured to learn the driver's behavior bymeasuring at least one of a vehicle speed selected by the driver, alateral control of the vehicle selected by the driver, and anacceleration or deceleration of the vehicle selected by the driver whenthe driver navigates the vehicle over the road segment.
 19. The vehicleof claim 15, wherein the threshold associated with the road segmentincludes a safety limit of the road segment, and the processor isconfigured to create the behavior policy by modifying the learneddriver's behavior based on the safety limit of the road segment.
 20. Thevehicle of claim 15, wherein the processor is further configured tolearn the driver's behavior in one of an offline learning mode in whichthe vehicle is driven by the driver and an online learning mode in whichthe vehicle is controlled by the processor as the driver operates acontrol of the vehicle.